Object Detection

Enumerations
enum	cv::TemplateMatchModes {   cv::TM_SQDIFF = 0,   cv::TM_SQDIFF_NORMED = 1,   cv::TM_CCORR = 2,   cv::TM_CCORR_NORMED = 3,   cv::TM_CCOEFF = 4,   cv::TM_CCOEFF_NORMED = 5 }
	type of the template matching operation More...

Functions
void	cv::matchTemplate (InputArray image, InputArray templ, OutputArray result, int method, InputArray mask=noArray())
	Compares a template against overlapped image regions. More...

Detailed Description

Enumeration Type Documentation

TemplateMatchModes

enum cv::TemplateMatchModes

type of the template matching operation

Enumerator
TM_SQDIFF  Python: cv.TM_SQDIFF	\[R(x,y)= \sum _{x',y'} (T(x',y')-I(x+x',y+y'))^2\]
TM_SQDIFF_NORMED  Python: cv.TM_SQDIFF_NORMED	\[R(x,y)= \frac{\sum_{x',y'} (T(x',y')-I(x+x',y+y'))^2}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}\]
TM_CCORR  Python: cv.TM_CCORR	\[R(x,y)= \sum _{x',y'} (T(x',y') \cdot I(x+x',y+y'))\]
TM_CCORR_NORMED  Python: cv.TM_CCORR_NORMED	\[R(x,y)= \frac{\sum_{x',y'} (T(x',y') \cdot I(x+x',y+y'))}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}\]
TM_CCOEFF  Python: cv.TM_CCOEFF	\[R(x,y)= \sum _{x',y'} (T'(x',y') \cdot I'(x+x',y+y'))\] where \[\begin{array}{l} T'(x',y')=T(x',y') - 1/(w \cdot h) \cdot \sum _{x'',y''} T(x'',y'') \\ I'(x+x',y+y')=I(x+x',y+y') - 1/(w \cdot h) \cdot \sum _{x'',y''} I(x+x'',y+y'') \end{array}\]
TM_CCOEFF_NORMED  Python: cv.TM_CCOEFF_NORMED	\[R(x,y)= \frac{ \sum_{x',y'} (T'(x',y') \cdot I'(x+x',y+y')) }{ \sqrt{\sum_{x',y'}T'(x',y')^2 \cdot \sum_{x',y'} I'(x+x',y+y')^2} }\]

Function Documentation

matchTemplate()

void cv::matchTemplate	(	InputArray	image,
		InputArray	templ,
		OutputArray	result,
		int	method,
		InputArray	mask = noArray()
	)

Python:
	result	=	cv.matchTemplate(	image, templ, method[, result[, mask]]	)

Compares a template against overlapped image regions.

The function slides through image , compares the overlapped patches of size \(w \times h\) against templ using the specified method and stores the comparison results in result . Here are the formulae for the available comparison methods ( \(I\) denotes image, \(T\) template, \(R\) result ). The summation is done over template and/or the image patch: \(x' = 0...w-1, y' = 0...h-1\)

After the function finishes the comparison, the best matches can be found as global minimums (when TM_SQDIFF was used) or maximums (when TM_CCORR or TM_CCOEFF was used) using the minMaxLoc function. In case of a color image, template summation in the numerator and each sum in the denominator is done over all of the channels and separate mean values are used for each channel. That is, the function can take a color template and a color image. The result will still be a single-channel image, which is easier to analyze.

Parameters

image	Image where the search is running. It must be 8-bit or 32-bit floating-point.
templ	Searched template. It must be not greater than the source image and have the same data type.
result	Map of comparison results. It must be single-channel 32-bit floating-point. If image is \(W \times H\) and templ is \(w \times h\) , then result is \((W-w+1) \times (H-h+1)\) .
method	Parameter specifying the comparison method, see TemplateMatchModes
mask	Mask of searched template. It must have the same datatype and size with templ. It is not set by default. Currently, only the TM_SQDIFF and TM_CCORR_NORMED methods are supported.

Examples:

samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp.